Personal authentication based on biometric verification is gaining increasing significance, with iris recognition in particular proving to be more accurate than other biometrics. Despite significant advances over the past decade, the need for robust iris recognition systems in the presence of variability in image size, position, and orientation still persists. Changes in position and size may be readily normalized in the pre-processing stage as they depend mainly on optical magnification and distance of the camera from the eye. It is also possible to compensate for non-affine pattern deformations and variations in pupil size by dilation within the iris. Iris orientation, on the other hand, depends upon a large number of internal and external factors including torsional eye rotation and head tilt. Optical systems may introduce image rotation depending on eye position, camera position, and mirror angles. Most present-day matching systems rotate the iris image by various amounts about the captured orientation to generate an array of feature vectors which are compared separately to find the best match.
One approach is discussed in J. Daugman, “The importance of being random: Statistical principles of iris recognition,” Pattern Recognition, vol. 36, pp. 279-291, 2003. Daugman computes the iris code in a single canonical orientation and compares it with several orientations by cyclic scrolling. The use of multiple comparisons leads to higher storage requirements and increased time to enrol and verify.
Correlation filters are known to offer good matching performance in the presence of image variability, and several researchers have investigated the use of correlation for biometric authentication; see for example R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proc. of the IEEE, vol. 85, pp. 1348-1363, 1997. L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient iris recognition by characterizing key local variations,” IEEE Trans. on Image Processing, vol. 13, pp. 739-750, 2004. Others have used phase based image matching to achieve good results in fingerprint and iris recognition; see for example Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki, Koji Kobayashi, and H. Nakajima, “An Efficient Iris Recognition Algorithm Using Phase-Based Image Matching,” Proc, IEEE International Conference on Image Processing, Genoa, 2005. and Koichi Ito, Ayumi Morita, Takafumi Aoki, Tatsuo Higuchi, Hiroshi Nakajima, and K. Kobayashi, “A Fingerprint Recognition Algorithm Using Phase-Based Image Matching for Low-Quality Fingerprints,” Proc. IEEE International Conference on Image Processing, Genoa, 2005.
In all such work the 2D cross-correlation techniques used require the storage of the entire database of images along with their iris codes. The operations described are computationally intensive, and typically affect the speed of the verification/identification process significantly.